Journal
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
Volume 19, Issue 6, Pages 1034-1047Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TVCG.2012.172
Keywords
Information visualization; visual analytics; graph visualization; community structure
Categories
Funding
- US National Science Foundation (NSF) [IIS-0946400, SBE-0915528, IIS-0916131]
- National Natural Science Foundation of China Project [60903062, 61170204]
- DHS Visual Analytics for Command, Control, and Interoperability (VACCINE) Center of Excellence under SouthEast Regional Visual Analytics Center
- Direct For Social, Behav & Economic Scie [0915528] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [0916131] Funding Source: National Science Foundation
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Community structure is an important characteristic of many real networks, which shows high concentrations of edges within special groups of vertices and low concentrations between these groups. Community related graph analysis, such as discovering relationships among communities, identifying attribute-structure relationships, and selecting a large number of vertices with desired structural features and attributes, are common tasks in knowledge discovery in such networks. The clutter and the lack of interactivity often hinder efforts to apply traditional graph visualization techniques in these tasks. In this paper, we propose PIWI, a novel graph visual analytics approach to these tasks. Instead of using Node-Link Diagrams (NLDs), PIWI provides coordinated, uncluttered visualizations, and novel interactions based on graph community structure. The novel features, applicability, and limitations of this new technique have been discussed in detail. A set of case studies and preliminary user studies have been conducted with real graphs containing thousands of vertices, which provide supportive evidence about the usefulness of PIWI in community related tasks.
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